Batch Auto-Labeling
Run models at scale with workflow Model stages, human correction routes, queues, monitoring, and failure recovery.
Batch Auto-Labeling moves model inference from an annotator’s current item into a repeatable project workflow. Unitlab uses a Model stage to run the selected model, save its predictions into annotation history, and route the item to human annotation, review, another automated stage, or a terminal state.
Recommended production pattern
A reliable first rollout uses Model → Annotate → Review. Direct Model → Review routing is appropriate only after the model is calibrated on the target domain and the review team can detect systematic errors.
Before you start
A project with representative data and an approved ontology.
A public or private AI model whose input and output contract matches the data.
A class mapping from model outputs to ontology classes.
Defined thresholds, failure ownership, and human acceptance criteria.
Permission to edit and apply the project workflow.
Configure batch auto-labeling
Select or integrate the model
Open Public AI Models or My AI Models. Confirm running state, supported data, output geometry, version, and owner. See Bring your own Models for private endpoints.
Model-stage configuration
Model and version
Pin the approved integration and record its owner
Input data
Match image, video, audio, text, or medical support
Output mapping
Map every emitted class and geometry intentionally
Threshold
Calibrate on the target domain; do not copy a generic default
Queue scope
Start with a representative batch or selected queue
Success route
Prefer human correction or review before Complete
Failure route
Keep failures visible and recoverable
Change control
Re-test after endpoint, model, prompt, mapping, or ontology changes
Monitor the run
A Model-stage item shows Processing while inference runs. Successful predictions are saved as normal annotation history and advance through the configured route. A failed item moves to an explicit error state.
Track at least:
total items entering the Model stage;
completed, processing, and failed counts;
empty-output rate;
per-class proposal count;
correction and deletion rate;
reviewer rejection rate;
latency and timeout rate;
model and ontology version.
Batch throughput is not quality. Approve scale only after the correction rate, missed-instance rate, and failure behavior are stable on representative data.
Safe rollout
Contract test
A few known items
Request, response, mapping, and failure states are valid
Calibration
Representative cohort
Threshold and class behavior are acceptable
Controlled batch
One queue or source slice
Human correction is stable and failures are owned
Production
Approved population
Monitoring, review, rollback, and provenance are operating
Recovery
Fix the model endpoint or mapping before retrying failed items.
Inspect remote state before repeating a mutation to avoid duplicate work.
Re-run only the affected cohort when possible.
If a workflow change would reset in-flight work, review the impact count and schedule the change.
Preserve model version and correction evidence in the release record.